Development of a Genetic Algorithm Based Geodesic Active Contour for Iris Based Ethnicity Prediction System

Abstract

The developments in science and technology have made it possible to use biometrics in applications where it is required to establish or confirm the identity of individuals. Among all possible biometric characteristics, the use of iris texture for recognition of individuals has been proven to be highly reliable. However, existing iris prediction systems have suffered from inability to handle more constrained acquisition (processing non-ideal iris images), high processing time and inappropriate parameter settings which usually results in inaccurate segmentation and poor classification results. This research therefore developed an improved segmentation and classification algorithms for iris-based ethnicity prediction system featuring the three major tribes in Nigeria. Six hundred (600) iris images from three major tribes in Nigeria (Yoruba, Hausa and Ibo) were locally captured for the database. Genetic Algorithm based Geodesic Active Contour (GAGAC) and standard Geodesic Active Control (GAC) were used for iris segmentation while Standard Support Vector Machine (SVM) and Galactic Swarm Optimisation SVM (GSOSVM) was used for iris classification. GAGAC and GSOSVM were used in the designing of the iris-based ethnicity prediction system at segmentation and classification stage. The developed iris-based ethnicity prediction system gave an improved predictive performance over the conventional one. The developed system can be used in different areas where higher security authentication is required.